Recently, DNA oligonucleotide microarray gene expression analysis has been used to define molecular profiles of medulloblastomas, the most common malignant brain tumors of childhood (Pomeroy et al., 2002). Medulloblastomas were found to be molecularly distinct from other CNS embryonal tumors, and their histological subtypes had unique and functionally significant gene expression patterns. Gene expression profiles were highly predictive of response to therapy, predicting survival in a retrospective analysis with much greater accuracy than current clinical staging criteria or single marker gene outcome predictors. These results must now be prospectively validated before molecular markers can be used for risk stratification in future clinical trials. The experiments of this proposal are correlative biological studies of Phase III medulloblastoma therapy trials of the Children's Oncology Group (COG), designed to optimize and validate prognostic molecular markers. In Specific Aim 1, a multi-molecular outcome predictor, based on gene expression profiles (Affymetrix Human Genome U133 arrays) of tumors from 500 children treated in COG medulloblastoma trials over the next 5 years, will be optimized, validated and developed for "real time" analysis in the context of future clinical trials. In Specific Aim 2, the gene expression database will be used to develop a molecular taxonomy, identifying subclasses of medulloblastomas defined by the expression profiles of molecular signaling pathways that promote tumorigenesis. Gene expression will be linked to oncogenic genomic mutations in Specific Aim 3, by combining expression profiling with genome-wide mutation analysis obtained from DNA BAC clone (Spectral Genomics) microarray-based comparative genomic hybridization. In achieving these Aims, we will create a comprehensive medulloblastoma gene expression and mutation database that is linked to clinical outcome of a large and well-characterized cohort of children. The data will hosted on the website of the NCI/NINDS Glioma Molecular Diagnostic Initiative that is being developed by Howard Fine of the NCI/NINDS Neuro-Oncology Branch and the NIH Bioinformatics group, so that investigators throughout the world can have free access to the molecular and clinical database generated by this project.